Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality.
Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality.
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DOI：
10.1186/1472-6963-10-245
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发表时间：
2010-08-20
影响因子：
2.8
通讯作者：
Doshi JA
中科院分区：
文献类型：
--
作者：
Li P;Kim MM;Doshi JA
医疗保险和医疗补助服务中心（CMS）已经实施了CMS-分层疾病类别（CMS- hcc）模型来风险调整医疗保险的人均支付。本研究旨在评估CMS-HCC风险调整方法的性能，并将其与Charlson和Elixhauser合并症测量方法在预测医疗保险受益人住院和6个月死亡率方面进行比较。该研究使用了2005-2006年慢性病数据仓库（CCW） 5%的医疗保险档案。主要研究样本包括2006年1月1日至2006年6月30日住院的所有社区居住的按服务收费的医疗保险受益人。此外，还选择了主要诊断为充血性心力衰竭（CHF）、中风、糖尿病（DM）和急性心肌梗死（AMI）的患者组成的四个疾病特异性样本亚组。通过提取每个患者的住院和/或门诊索赔，为每个样本生成四个分析文件。采用Logistic回归对两种方法进行比较。采用c-统计量、赤池信息准则（AIC）、贝叶斯信息准则（BIC）及其95%置信区间进行模型性能评估。在包括指数住院索赔的分析文件中的所有样本中，CMS-HCC在预测住院和6个月死亡率方面的c-统计值比Charlson和Elixhauser方法高，AIC和BIC值比Charlson和Elixhauser方法低。排除指数住院索赔通常会导致所有方法的模型性能下降，其中CMS-HCC方法下降幅度最大。然而，CMS-HCC仍然与其他两种方法表现相同或更好。与Charlson和Elixhauser方法相比，CMS-HCC方法在预测住院死亡率和6个月死亡率方面表现更好。如果患者在指数住院前的诊断信息可用并用于编码风险调整因子，则CMS-HCC模型优于Charlson和Elixhauser方法。然而，在评估住院治疗过程和索引前入院诊断数据不可用的研究中，应谨慎行事。
The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries. The study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1st, 2006 and June 30th, 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping. The CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods. The CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods if information about the patient's diagnoses prior to the index hospitalization is available and used to code the risk adjusters. However, caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable.
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影响因子：
7.2
作者：
DEYO, RA;CHERKIN, DC;CIOL, MA
通讯作者：
CIOL, MA
影响因子：
7.2
作者：
Klabunde, CN;Potosky, AL;Warren, JL
通讯作者：
Warren, JL
影响因子：
3
作者：
Rosen, AK;Rivard, P;Romano, PS
通讯作者：
Romano, PS
影响因子：
3
作者：
Elixhauser, A;Steiner, C;Coffey, RN
通讯作者：
Coffey, RN
影响因子：
3
作者：
Stukenborg, GJ;Wagner, DP;Connors, AF
通讯作者：
Connors, AF